Calculate Contextual Importance (CI) and Contextual Utility (CU) for an instance (Context) using the given "black-box" model.
ciu.explain(
ciu,
instance,
ind.inputs.to.explain,
in.min.max.limits = NULL,
n.samples = 100,
target.concept = NULL,
target.ciu = NULL
)
ciu.result
object.
ciu
object as created with ciu function (not to be confused
with CIU
object as created by ciu.new).
Input values for the instance to explain. Should be a
data.frame even though a vector
or matrix
might work too if input
names and other needed metadata can be deduced from the dataset or other
parameters given to ciu.new
.
vector of indices for the inputs to be
explained, i.e. for which CIU should be calculated. If NULL
, then all
inputs will be included.
data.frame or matrix with one row per output and two columns, where the first column indicates the minimal value and the second column the maximal value for that output. ONLY NEEDED HERE IF not given as parameter to ciu.new or if the limits are different for this specific instance than the default ones.
How many instances to generate for estimating CI and CU. For inputs of type factor, all possible combinations of input values are generated, so this parameter only influences how many instances are (at least) generated for continuous-valued inputs.
If provided, then calculate CIU of inputs
ind.inputs.to.explain
relative to the given concept rather than
relative to the actual output(s). ind.inputs.to.explain
should
normally be a subset (or all) of the inputs that target.concept
consists of, even though that not required by the CIU calculation.
If a target.ciu
is provided, then the target.concept
doesn't have to
be included in the vocabulary
gives as parameter to ciu.new
(at least for the moment).
ciu.result
object previously calculated for
target.concept
. If a target.concept
is provided but target.ciu=NULL
,
then target.ciu
is estimated by a call to ciu.explain with the
n.samples
value given as a parameter to this call. It may be useful
to provide target.ciu
if it should be estimated using some other
(typically greater) value for n.samples
than the default one, or if it
has already been calculated for some reason.
Kary Främling